
import pandas as pd
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import plotly.express as px
from sklearn.model_selection import train_test_split
from keras.preprocessing.sequence import TimeseriesGenerator
from sklearn.preprocessing import MinMaxScaler, StandardScaler
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
import seaborn as sns
import statsmodels.api as sm

mpl.rcParams['figure.figsize'] = (10, 8)
mpl.rcParams['axes.grid'] = False


df_power= pd.read_csv('power_actual.csv')


df_weather=pd.read_csv('weather_actuals.csv')


df_power.isnull().sum()

Unnamed: 0        0
datetime          0
ghi           35040
gti           35040
power             0
dtype: int64


df_power.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 70080 entries, 0 to 70079
Data columns (total 5 columns):
 #   Column      Non-Null Count  Dtype  
---  ------      --------------  -----  
 0   Unnamed: 0  70080 non-null  int64  
 1   datetime    70080 non-null  object 
 2   ghi         35040 non-null  float64
 3   gti         35040 non-null  float64
 4   power       70080 non-null  float64
dtypes: float64(3), int64(1), object(1)
memory usage: 2.7+ MB


df_power.replace(np.nan,method='ffill',inplace=True)

Unnamed: 0    None
datetime      None
ghi           None
gti           None
power         None
dtype: object


df_power.describe()


df_power['datetime']=pd.to_datetime(df_power['datetime'])


df_power.describe()


df_power.tail()


df_power.max()

Unnamed: 0                  70079
datetime      2019-09-30 23:45:00
ghi                             0
gti                             0
power                       16172
dtype: object


fig=px.line(df_power,x='datetime',y='power',title='Energy generation')

fig.update_xaxes(
    rangeslider_visible= True,
    rangeselector=dict(
                        buttons = list([
                        dict(count = 1,label = '1m',step='month',stepmode = "backward"),
                        dict(count = 2,label = '6m',step='month',stepmode = "backward"),
                        dict(count = 3,label = '6mYTD',step='month',stepmode = "todate"),
                        dict(count = 4,label = '2y',step='year',stepmode = "backward"),
                        dict(count = 5,label = '3y',step='year',stepmode = "backward"),
                        dict(step= 'all')
                            ])        
                        )
                   )
fig.show()


df_power.set_index('datetime')


df_power.plot(subplots=True)

array([<AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>,
       <AxesSubplot:>], dtype=object)


df_power=df_power.drop(['ghi', 'gti','Unnamed: 0'], axis=1)


pd.date_range(start = '2017-10-01', end = '2019-09-30' ).difference(df_power.index)

DatetimeIndex(['2017-10-01', '2017-10-02', '2017-10-03', '2017-10-04',
               '2017-10-05', '2017-10-06', '2017-10-07', '2017-10-08',
               '2017-10-09', '2017-10-10',
               ...
               '2019-09-21', '2019-09-22', '2019-09-23', '2019-09-24',
               '2019-09-25', '2019-09-26', '2019-09-27', '2019-09-28',
               '2019-09-29', '2019-09-30'],
              dtype='datetime64[ns]', length=730, freq=None)


df_n=df_power.copy()


df = df_power.resample('1H', on='datetime').mean()


df.head(10)


df.max()

power    15091.685
dtype: float64


df.info()

<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 17520 entries, 2017-10-01 00:00:00 to 2019-09-30 23:00:00
Freq: H
Data columns (total 1 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   power   17520 non-null  float64
dtypes: float64(1)
memory usage: 273.8 KB


df_weather.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 13619 entries, 0 to 13618
Data columns (total 31 columns):
 #   Column                Non-Null Count  Dtype  
---  ------                --------------  -----  
 0   Unnamed: 0            13619 non-null  int64  
 1   plant_id              13619 non-null  int64  
 2   datetime_utc          13619 non-null  object 
 3   datetime_local        13619 non-null  object 
 4   cloud_cover           13619 non-null  float64
 5   apparent_temperature  13619 non-null  float64
 6   temperature           13619 non-null  int64  
 7   humidity              13619 non-null  int64  
 8   dew_point             13619 non-null  float64
 9   wind_bearing          13619 non-null  int64  
 10  wind_speed            13619 non-null  float64
 11  wind_chill            3744 non-null   float64
 12  wind_gust             13619 non-null  float64
 13  heat_index            3744 non-null   float64
 14  pressure              13619 non-null  float64
 15  qpf                   3744 non-null   float64
 16  uv_index              13619 non-null  int64  
 17  snow                  3744 non-null   float64
 18  pop                   3744 non-null   float64
 19  fctcode               3744 non-null   float64
 20  ozone                 13619 non-null  float64
 21  precip_accumulation   3744 non-null   float64
 22  precip_intensity      13619 non-null  float64
 23  precip_probability    13619 non-null  float64
 24  precip_type           6490 non-null   object 
 25  visibility            13619 non-null  float64
 26  sunrise               13619 non-null  object 
 27  sunset                13619 non-null  object 
 28  icon                  13619 non-null  object 
 29  summary               13619 non-null  object 
 30  updated_at            13619 non-null  object 
dtypes: float64(17), int64(6), object(8)
memory usage: 3.2+ MB


df_weather


df_weather.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 13619 entries, 0 to 13618
Data columns (total 31 columns):
 #   Column                Non-Null Count  Dtype  
---  ------                --------------  -----  
 0   Unnamed: 0            13619 non-null  int64  
 1   plant_id              13619 non-null  int64  
 2   datetime_utc          13619 non-null  object 
 3   datetime_local        13619 non-null  object 
 4   cloud_cover           13619 non-null  float64
 5   apparent_temperature  13619 non-null  float64
 6   temperature           13619 non-null  int64  
 7   humidity              13619 non-null  int64  
 8   dew_point             13619 non-null  float64
 9   wind_bearing          13619 non-null  int64  
 10  wind_speed            13619 non-null  float64
 11  wind_chill            3744 non-null   float64
 12  wind_gust             13619 non-null  float64
 13  heat_index            3744 non-null   float64
 14  pressure              13619 non-null  float64
 15  qpf                   3744 non-null   float64
 16  uv_index              13619 non-null  int64  
 17  snow                  3744 non-null   float64
 18  pop                   3744 non-null   float64
 19  fctcode               3744 non-null   float64
 20  ozone                 13619 non-null  float64
 21  precip_accumulation   3744 non-null   float64
 22  precip_intensity      13619 non-null  float64
 23  precip_probability    13619 non-null  float64
 24  precip_type           6490 non-null   object 
 25  visibility            13619 non-null  float64
 26  sunrise               13619 non-null  object 
 27  sunset                13619 non-null  object 
 28  icon                  13619 non-null  object 
 29  summary               13619 non-null  object 
 30  updated_at            13619 non-null  object 
dtypes: float64(17), int64(6), object(8)
memory usage: 3.2+ MB


df_weather.precip_type.value_counts()

rain     6147
-9999     343
Name: precip_type, dtype: int64


df_weather.precip_type.unique()

array(['rain', '-9999', nan], dtype=object)


df_weather.corr()


data_obj=df_weather.select_dtypes(include=[object])
data_nonobj=df_weather.select_dtypes(exclude=[object])


data_obj.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 13619 entries, 0 to 13618
Data columns (total 8 columns):
 #   Column          Non-Null Count  Dtype 
---  ------          --------------  ----- 
 0   datetime_utc    13619 non-null  object
 1   datetime_local  13619 non-null  object
 2   precip_type     6490 non-null   object
 3   sunrise         13619 non-null  object
 4   sunset          13619 non-null  object
 5   icon            13619 non-null  object
 6   summary         13619 non-null  object
 7   updated_at      13619 non-null  object
dtypes: object(8)
memory usage: 851.3+ KB


data_nonobj.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 13619 entries, 0 to 13618
Data columns (total 23 columns):
 #   Column                Non-Null Count  Dtype  
---  ------                --------------  -----  
 0   Unnamed: 0            13619 non-null  int64  
 1   plant_id              13619 non-null  int64  
 2   cloud_cover           13619 non-null  float64
 3   apparent_temperature  13619 non-null  float64
 4   temperature           13619 non-null  int64  
 5   humidity              13619 non-null  int64  
 6   dew_point             13619 non-null  float64
 7   wind_bearing          13619 non-null  int64  
 8   wind_speed            13619 non-null  float64
 9   wind_chill            3744 non-null   float64
 10  wind_gust             13619 non-null  float64
 11  heat_index            3744 non-null   float64
 12  pressure              13619 non-null  float64
 13  qpf                   3744 non-null   float64
 14  uv_index              13619 non-null  int64  
 15  snow                  3744 non-null   float64
 16  pop                   3744 non-null   float64
 17  fctcode               3744 non-null   float64
 18  ozone                 13619 non-null  float64
 19  precip_accumulation   3744 non-null   float64
 20  precip_intensity      13619 non-null  float64
 21  precip_probability    13619 non-null  float64
 22  visibility            13619 non-null  float64
dtypes: float64(17), int64(6)
memory usage: 2.4 MB


df_weather=df_weather.drop(['datetime_utc','plant_id','Unnamed: 0','updated_at'], axis=1)


df_weather.head()


df_weather['datetime_local']=pd.to_datetime(df_weather['datetime_local'])


df_weather['sunrise']=pd.to_datetime(df_weather['sunrise'])


df_weather['sunset']=pd.to_datetime(df_weather['sunset'])


df_weather.set_index('datetime_local',inplace=True)


df_weather.describe()


fe = df_weather.groupby('icon').size()/len(df_weather)
df_weather.loc[:,'icon']=df_weather['icon'].map(fe)

fe

icon
clear-day              0.220060
clear-night            0.225788
cloudy                 0.095822
fog                    0.214406
partly-cloudy-day      0.129819
partly-cloudy-night    0.107056
rain                   0.006902
wind                   0.000147
dtype: float64


fe.plot()

<AxesSubplot:xlabel='icon'>


fe_s = df_weather.groupby('summary').size()/len(df_weather)
df_weather.loc[:,'summary']=df_weather['summary'].map(fe_s)


fe_s

summary
Breezy and Foggy                 0.000147
Clear                            0.408767
Foggy                            0.214259
Humid                            0.037081
Humid and Foggy                  0.000147
Humid and Mostly Cloudy          0.060651
Humid and Overcast               0.052427
Humid and Partly Cloudy          0.042808
Light Rain                       0.000073
Light Rain and Humid             0.000294
Mostly Cloudy                    0.046846
Overcast                         0.043395
Partly Cloudy                    0.086570
Possible Drizzle                 0.000073
Possible Drizzle and Humid       0.000073
Possible Light Rain              0.001469
Possible Light Rain and Humid    0.003818
Rain                             0.000147
Rain and Humid                   0.000955
dtype: float64


fe_s.plot()

<AxesSubplot:xlabel='summary'>


fe_p = df_weather.groupby('precip_type').size()/len(df_weather)
df_weather.loc[:,'precip_type']=df_weather['precip_type'].map(fe_p)


fe_p

precip_type
-9999    0.025185
rain     0.451355
dtype: float64


df_weather.head()


df_weather.info()

<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 13619 entries, 2017-10-01 00:00:00 to 2019-09-30 23:00:00
Data columns (total 26 columns):
 #   Column                Non-Null Count  Dtype         
---  ------                --------------  -----         
 0   cloud_cover           13619 non-null  float64       
 1   apparent_temperature  13619 non-null  float64       
 2   temperature           13619 non-null  int64         
 3   humidity              13619 non-null  int64         
 4   dew_point             13619 non-null  float64       
 5   wind_bearing          13619 non-null  int64         
 6   wind_speed            13619 non-null  float64       
 7   wind_chill            3744 non-null   float64       
 8   wind_gust             13619 non-null  float64       
 9   heat_index            3744 non-null   float64       
 10  pressure              13619 non-null  float64       
 11  qpf                   3744 non-null   float64       
 12  uv_index              13619 non-null  int64         
 13  snow                  3744 non-null   float64       
 14  pop                   3744 non-null   float64       
 15  fctcode               3744 non-null   float64       
 16  ozone                 13619 non-null  float64       
 17  precip_accumulation   3744 non-null   float64       
 18  precip_intensity      13619 non-null  float64       
 19  precip_probability    13619 non-null  float64       
 20  precip_type           6490 non-null   float64       
 21  visibility            13619 non-null  float64       
 22  sunrise               13619 non-null  datetime64[ns]
 23  sunset                13619 non-null  datetime64[ns]
 24  icon                  13619 non-null  float64       
 25  summary               13619 non-null  float64       
dtypes: datetime64[ns](2), float64(20), int64(4)
memory usage: 2.8 MB


df_weather.isnull().sum()

cloud_cover                0
apparent_temperature       0
temperature                0
humidity                   0
dew_point                  0
wind_bearing               0
wind_speed                 0
wind_chill              9875
wind_gust                  0
heat_index              9875
pressure                   0
qpf                     9875
uv_index                   0
snow                    9875
pop                     9875
fctcode                 9875
ozone                      0
precip_accumulation     9875
precip_intensity           0
precip_probability         0
precip_type             7129
visibility                 0
sunrise                    0
sunset                     0
icon                       0
summary                    0
dtype: int64


df_weather['precip_type'].value_counts()

0.451355    6147
0.025185     343
Name: precip_type, dtype: int64


df_weather.describe()


df_weather=df_weather.drop(['sunrise','sunset'],axis=1)


df_weather.head()


df_weather.describe(include='all')


df_weather.replace(-9999, np.nan,inplace=True)
df_weather.fillna(0)


df_weather.describe()


df_weather.isnull().sum()

cloud_cover               428
apparent_temperature        0
temperature                 0
humidity                    0
dew_point                   0
wind_bearing              919
wind_speed                740
wind_chill              13619
wind_gust                3264
heat_index              13619
pressure                 2156
qpf                     13619
uv_index                 3264
snow                    13619
pop                     13619
fctcode                 13619
ozone                    3264
precip_accumulation     13619
precip_intensity         3264
precip_probability       3264
precip_type              7129
visibility                176
icon                        0
summary                     0
dtype: int64


df_weather.info()

<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 13619 entries, 2017-10-01 00:00:00 to 2019-09-30 23:00:00
Data columns (total 24 columns):
 #   Column                Non-Null Count  Dtype  
---  ------                --------------  -----  
 0   cloud_cover           13191 non-null  float64
 1   apparent_temperature  13619 non-null  float64
 2   temperature           13619 non-null  int64  
 3   humidity              13619 non-null  int64  
 4   dew_point             13619 non-null  float64
 5   wind_bearing          12700 non-null  float64
 6   wind_speed            12879 non-null  float64
 7   wind_chill            0 non-null      float64
 8   wind_gust             10355 non-null  float64
 9   heat_index            0 non-null      float64
 10  pressure              11463 non-null  float64
 11  qpf                   0 non-null      float64
 12  uv_index              10355 non-null  float64
 13  snow                  0 non-null      float64
 14  pop                   0 non-null      float64
 15  fctcode               0 non-null      float64
 16  ozone                 10355 non-null  float64
 17  precip_accumulation   0 non-null      float64
 18  precip_intensity      10355 non-null  float64
 19  precip_probability    10355 non-null  float64
 20  precip_type           6490 non-null   float64
 21  visibility            13443 non-null  float64
 22  icon                  13619 non-null  float64
 23  summary               13619 non-null  float64
dtypes: float64(22), int64(2)
memory usage: 2.6 MB


f, ax = plt.subplots(figsize=(16,9))
sns.heatmap(df_weather.corr(),cmap="YlGnBu",annot=True)

<AxesSubplot:>


df_weather.info()

<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 13619 entries, 2017-10-01 00:00:00 to 2019-09-30 23:00:00
Data columns (total 24 columns):
 #   Column                Non-Null Count  Dtype  
---  ------                --------------  -----  
 0   cloud_cover           13191 non-null  float64
 1   apparent_temperature  13619 non-null  float64
 2   temperature           13619 non-null  int64  
 3   humidity              13619 non-null  int64  
 4   dew_point             13619 non-null  float64
 5   wind_bearing          12700 non-null  float64
 6   wind_speed            12879 non-null  float64
 7   wind_chill            0 non-null      float64
 8   wind_gust             10355 non-null  float64
 9   heat_index            0 non-null      float64
 10  pressure              11463 non-null  float64
 11  qpf                   0 non-null      float64
 12  uv_index              10355 non-null  float64
 13  snow                  0 non-null      float64
 14  pop                   0 non-null      float64
 15  fctcode               0 non-null      float64
 16  ozone                 10355 non-null  float64
 17  precip_accumulation   0 non-null      float64
 18  precip_intensity      10355 non-null  float64
 19  precip_probability    10355 non-null  float64
 20  precip_type           6490 non-null   float64
 21  visibility            13443 non-null  float64
 22  icon                  13619 non-null  float64
 23  summary               13619 non-null  float64
dtypes: float64(22), int64(2)
memory usage: 2.6 MB


df1=df_weather.copy()


df1.fillna(0,inplace=True)


df1.drop(['precip_accumulation','fctcode','pop','snow','qpf','heat_index','wind_chill'],inplace=True,axis=1)


df1.info()

<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 13619 entries, 2017-10-01 00:00:00 to 2019-09-30 23:00:00
Data columns (total 17 columns):
 #   Column                Non-Null Count  Dtype  
---  ------                --------------  -----  
 0   cloud_cover           13619 non-null  float64
 1   apparent_temperature  13619 non-null  float64
 2   temperature           13619 non-null  int64  
 3   humidity              13619 non-null  int64  
 4   dew_point             13619 non-null  float64
 5   wind_bearing          13619 non-null  float64
 6   wind_speed            13619 non-null  float64
 7   wind_gust             13619 non-null  float64
 8   pressure              13619 non-null  float64
 9   uv_index              13619 non-null  float64
 10  ozone                 13619 non-null  float64
 11  precip_intensity      13619 non-null  float64
 12  precip_probability    13619 non-null  float64
 13  precip_type           13619 non-null  float64
 14  visibility            13619 non-null  float64
 15  icon                  13619 non-null  float64
 16  summary               13619 non-null  float64
dtypes: float64(15), int64(2)
memory usage: 1.9 MB


df1 = df1.resample('1H').pad()


df1.head()


df2= pd.concat([df,df1],axis=1)


df2.info()

<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 17520 entries, 2017-10-01 00:00:00 to 2019-09-30 23:00:00
Freq: H
Data columns (total 18 columns):
 #   Column                Non-Null Count  Dtype  
---  ------                --------------  -----  
 0   power                 17520 non-null  float64
 1   cloud_cover           17520 non-null  float64
 2   apparent_temperature  17520 non-null  float64
 3   temperature           17520 non-null  int64  
 4   humidity              17520 non-null  int64  
 5   dew_point             17520 non-null  float64
 6   wind_bearing          17520 non-null  float64
 7   wind_speed            17520 non-null  float64
 8   wind_gust             17520 non-null  float64
 9   pressure              17520 non-null  float64
 10  uv_index              17520 non-null  float64
 11  ozone                 17520 non-null  float64
 12  precip_intensity      17520 non-null  float64
 13  precip_probability    17520 non-null  float64
 14  precip_type           17520 non-null  float64
 15  visibility            17520 non-null  float64
 16  icon                  17520 non-null  float64
 17  summary               17520 non-null  float64
dtypes: float64(16), int64(2)
memory usage: 3.2 MB


df2.describe(include='all')


df2.head()


df2.to_csv('final_cleaned_data.csv')


pd.date_range(start = '2017-10-01', end = '2019-09-30' ).difference(df_weather.index)

DatetimeIndex(['2018-02-28', '2018-03-01', '2018-03-02', '2018-03-03',
               '2018-03-04', '2018-03-05', '2018-03-06', '2018-03-13',
               '2018-03-14', '2018-03-15',
               ...
               '2018-08-06', '2018-08-07', '2018-08-08', '2018-08-09',
               '2018-08-10', '2018-08-11', '2018-08-12', '2018-08-13',
               '2018-08-14', '2018-08-15'],
              dtype='datetime64[ns]', length=163, freq=None)


f, ax = plt.subplots(figsize=(16,9))
sns.heatmap(df2.corr(),cmap="YlGnBu",annot=True)

<AxesSubplot:>


energy_series = df2.loc[:, ('summary')]
energy_series.plot()

<AxesSubplot:xlabel='datetime'>


temperature_series=df2.loc[:,('temperature')]
temperature_series.plot()

<AxesSubplot:xlabel='datetime'>


power_series=df2.loc[:,('power')]
power_series.plot()

<AxesSubplot:xlabel='datetime'>


df_weather_forecast=pd.read_csv('weather_forecast.csv')


df_weather_forecast=df_weather_forecast.drop(['datetime_utc','plant_id','Unnamed: 0','updated_at','sunset','sunrise','plant_id'], axis=1)


df_weather_forecast['datetime_local']=pd.to_datetime(df_weather_forecast['datetime_local'])


df_weather_forecast.set_index('datetime_local',inplace=True)


pd.date_range(start = '2019-10-01', end = '2019-10-27' ).difference(df_weather_forecast.index)

DatetimeIndex([], dtype='datetime64[ns]', freq=None)


df_weather_forecast.drop(['precip_accumulation','fctcode','pop','snow','qpf','heat_index','wind_chill'],inplace=True,axis=1)


df_weather_forecast.shape

(648, 17)


df_weather_forecast['precip_type'].value_counts()

rain    114
Name: precip_type, dtype: int64


df_weather_forecast.fillna(0,axis=1,inplace=True)


df_weather_forecast.isnull().sum()

cloud_cover             0
apparent_temperature    0
temperature             0
humidity                0
dew_point               0
wind_bearing            0
wind_speed              0
wind_gust               0
pressure                0
uv_index                0
ozone                   0
precip_intensity        0
precip_probability      0
precip_type             0
visibility              0
icon                    0
summary                 0
dtype: int64


df_weather_forecast.info()

<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 648 entries, 2019-10-01 00:00:00 to 2019-10-27 23:00:00
Data columns (total 17 columns):
 #   Column                Non-Null Count  Dtype  
---  ------                --------------  -----  
 0   cloud_cover           648 non-null    float64
 1   apparent_temperature  648 non-null    float64
 2   temperature           648 non-null    int64  
 3   humidity              648 non-null    int64  
 4   dew_point             648 non-null    float64
 5   wind_bearing          648 non-null    int64  
 6   wind_speed            648 non-null    float64
 7   wind_gust             648 non-null    float64
 8   pressure              648 non-null    float64
 9   uv_index              648 non-null    int64  
 10  ozone                 648 non-null    float64
 11  precip_intensity      648 non-null    float64
 12  precip_probability    648 non-null    float64
 13  precip_type           648 non-null    object 
 14  visibility            648 non-null    float64
 15  icon                  648 non-null    object 
 16  summary               648 non-null    object 
dtypes: float64(10), int64(4), object(3)
memory usage: 91.1+ KB


se = df_weather_forecast.groupby('icon').size()/len(df_weather_forecast)
df_weather_forecast.loc[:,'icon']=df_weather_forecast['icon'].map(se)


se_s = df_weather_forecast.groupby('summary').size()/len(df_weather_forecast)
df_weather_forecast.loc[:,'summary']=df_weather_forecast['summary'].map(se_s)


se_p = df_weather_forecast.groupby('precip_type').size()/len(df_weather_forecast)
df_weather_forecast.loc[:,'precip_type']=df_weather_forecast['precip_type'].map(se_p)


df_weather_forecast.head()


df_weather_forecast['power']=''


df_weather_forecast['power']=pd.to_numeric(df_weather_forecast['power'],errors='coerce')


df_weather_forecast.info()

<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 648 entries, 2019-10-01 00:00:00 to 2019-10-27 23:00:00
Data columns (total 18 columns):
 #   Column                Non-Null Count  Dtype  
---  ------                --------------  -----  
 0   cloud_cover           648 non-null    float64
 1   apparent_temperature  648 non-null    float64
 2   temperature           648 non-null    int64  
 3   humidity              648 non-null    int64  
 4   dew_point             648 non-null    float64
 5   wind_bearing          648 non-null    int64  
 6   wind_speed            648 non-null    float64
 7   wind_gust             648 non-null    float64
 8   pressure              648 non-null    float64
 9   uv_index              648 non-null    int64  
 10  ozone                 648 non-null    float64
 11  precip_intensity      648 non-null    float64
 12  precip_probability    648 non-null    float64
 13  precip_type           648 non-null    float64
 14  visibility            648 non-null    float64
 15  icon                  648 non-null    float64
 16  summary               648 non-null    float64
 17  power                 0 non-null      float64
dtypes: float64(14), int64(4)
memory usage: 96.2 KB


df2.head()


df2.info()

<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 17520 entries, 2017-10-01 00:00:00 to 2019-09-30 23:00:00
Freq: H
Data columns (total 18 columns):
 #   Column                Non-Null Count  Dtype  
---  ------                --------------  -----  
 0   power                 17520 non-null  float64
 1   cloud_cover           17520 non-null  float64
 2   apparent_temperature  17520 non-null  float64
 3   temperature           17520 non-null  int64  
 4   humidity              17520 non-null  int64  
 5   dew_point             17520 non-null  float64
 6   wind_bearing          17520 non-null  float64
 7   wind_speed            17520 non-null  float64
 8   wind_gust             17520 non-null  float64
 9   pressure              17520 non-null  float64
 10  uv_index              17520 non-null  float64
 11  ozone                 17520 non-null  float64
 12  precip_intensity      17520 non-null  float64
 13  precip_probability    17520 non-null  float64
 14  precip_type           17520 non-null  float64
 15  visibility            17520 non-null  float64
 16  icon                  17520 non-null  float64
 17  summary               17520 non-null  float64
dtypes: float64(16), int64(2)
memory usage: 3.2 MB


train_data=df2.loc['2017-10-01':'2019-07-30']


val_data=df2.loc['2019-08-01':'2019-08-31']


test_data=df2.loc['2019-08-01':'2019-09-30']


from sklearn.preprocessing import MinMaxScaler, StandardScaler


clean_train_data=train_data.copy()


clean_val_data=val_data.copy()


clean_test_data=test_data.copy()


x_train,y_train=clean_train_data.drop(['power'],axis=1),clean_train_data.power


x_test, y_test=clean_test_data.drop(['power'],axis=1),clean_test_data.power


x_val, y_val=clean_val_data.drop(['power'],axis=1),clean_val_data.power


x_test.shape

(1464, 17)


x_train.shape

(16032, 17)


x_val.shape

(744, 17)


scaler=StandardScaler()


x_train_scaled=scaler.fit_transform(x_train)
x_test_scaled=scaler.fit_transform(x_test)
x_val_scaled=scaler.fit_transform(x_val)


x_train_reshaped=x_train_scaled.reshape((x_train_scaled.shape[0],1,x_train_scaled.shape[1]))


x_val_reshaped=x_val_scaled.reshape((x_val_scaled.shape[0],1,x_val_scaled.shape[1]))


from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM


model = tf.keras.Sequential()
model.add(tf.keras.layers.LSTM(25, input_shape= (1,17), return_sequences=True))
model.add(tf.keras.layers.ReLU()) 
model.add(tf.keras.layers.LSTM(25, return_sequences=True))
model.add(tf.keras.layers.ReLU()) 
model.add(tf.keras.layers.Dropout(0.1)) 
model.add(tf.keras.layers.LSTM(5, return_sequences=False))
model.add(tf.keras.layers.Dropout(0.1)) 
model.add(tf.keras.layers.Dense(3))


model.summary()

Model: "sequential_14"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
lstm_31 (LSTM)               (None, 1, 25)             4300      
_________________________________________________________________
re_lu_18 (ReLU)              (None, 1, 25)             0         
_________________________________________________________________
lstm_32 (LSTM)               (None, 1, 25)             5100      
_________________________________________________________________
re_lu_19 (ReLU)              (None, 1, 25)             0         
_________________________________________________________________
dropout_18 (Dropout)         (None, 1, 25)             0         
_________________________________________________________________
lstm_33 (LSTM)               (None, 5)                 620       
_________________________________________________________________
dropout_19 (Dropout)         (None, 5)                 0         
_________________________________________________________________
dense_13 (Dense)             (None, 3)                 18        
=================================================================
Total params: 10,038
Trainable params: 10,038
Non-trainable params: 0
_________________________________________________________________


early_stopping = tf.keras.callbacks.EarlyStopping(monitor='val_loss',
                                                    patience=2,
                                                    mode='min')

model.compile(loss=tf.losses.MeanSquaredError(),
              optimizer=tf.optimizers.Adam(lr=0.001),
              metrics=[tf.keras.metrics.MeanAbsoluteError()])

history = model.fit(x_train_reshaped,y_train, epochs=200,
                    validation_data=(x_val_reshaped,y_val),
                    shuffle=False)

Epoch 1/200
501/501 [==============================] - 3s 6ms/step - loss: 260931.2188 - mean_absolute_error: 31.2826 - val_loss: 17.9854 - val_mean_absolute_error: 3.0966
Epoch 2/200
501/501 [==============================] - 2s 3ms/step - loss: 260887.4219 - mean_absolute_error: 31.4072 - val_loss: 15.9234 - val_mean_absolute_error: 2.5712
Epoch 3/200
501/501 [==============================] - 2s 3ms/step - loss: 260859.0000 - mean_absolute_error: 31.2808 - val_loss: 15.1265 - val_mean_absolute_error: 2.6614
Epoch 4/200
501/501 [==============================] - 2s 3ms/step - loss: 260848.0156 - mean_absolute_error: 31.2866 - val_loss: 14.5729 - val_mean_absolute_error: 2.6283
Epoch 5/200
501/501 [==============================] - 2s 3ms/step - loss: 260834.9062 - mean_absolute_error: 31.2321 - val_loss: 14.0840 - val_mean_absolute_error: 2.5846
Epoch 6/200
501/501 [==============================] - 2s 3ms/step - loss: 260826.0000 - mean_absolute_error: 31.1672 - val_loss: 13.5341 - val_mean_absolute_error: 2.5088
Epoch 7/200
501/501 [==============================] - 2s 3ms/step - loss: 260805.5000 - mean_absolute_error: 31.0918 - val_loss: 13.0282 - val_mean_absolute_error: 2.4466
Epoch 8/200
501/501 [==============================] - 2s 3ms/step - loss: 260810.3125 - mean_absolute_error: 31.0358 - val_loss: 12.5177 - val_mean_absolute_error: 2.3698
Epoch 9/200
501/501 [==============================] - 2s 4ms/step - loss: 260787.3438 - mean_absolute_error: 30.9066 - val_loss: 12.1077 - val_mean_absolute_error: 2.3366
Epoch 10/200
501/501 [==============================] - 2s 3ms/step - loss: 260779.6875 - mean_absolute_error: 31.0137 - val_loss: 12.0622 - val_mean_absolute_error: 2.3720
Epoch 11/200
501/501 [==============================] - 2s 3ms/step - loss: 260761.4219 - mean_absolute_error: 30.9100 - val_loss: 11.6788 - val_mean_absolute_error: 2.3484
Epoch 12/200
501/501 [==============================] - 2s 3ms/step - loss: 260757.4844 - mean_absolute_error: 30.8369 - val_loss: 11.3210 - val_mean_absolute_error: 2.3158
Epoch 13/200
501/501 [==============================] - 2s 3ms/step - loss: 260733.6562 - mean_absolute_error: 30.8808 - val_loss: 11.0592 - val_mean_absolute_error: 2.2873
Epoch 14/200
501/501 [==============================] - 2s 3ms/step - loss: 260730.1250 - mean_absolute_error: 30.7857 - val_loss: 10.6745 - val_mean_absolute_error: 2.2449
Epoch 15/200
501/501 [==============================] - 2s 3ms/step - loss: 260730.0000 - mean_absolute_error: 30.9193 - val_loss: 10.6999 - val_mean_absolute_error: 2.1791
Epoch 16/200
501/501 [==============================] - 2s 3ms/step - loss: 260702.2812 - mean_absolute_error: 30.7441 - val_loss: 10.3054 - val_mean_absolute_error: 2.1640
Epoch 17/200
501/501 [==============================] - 2s 3ms/step - loss: 260702.4688 - mean_absolute_error: 30.7745 - val_loss: 10.0462 - val_mean_absolute_error: 2.1035
Epoch 18/200
501/501 [==============================] - 2s 3ms/step - loss: 260691.8281 - mean_absolute_error: 30.6575 - val_loss: 9.9316 - val_mean_absolute_error: 2.1207
Epoch 19/200
501/501 [==============================] - 2s 3ms/step - loss: 260669.3281 - mean_absolute_error: 30.8484 - val_loss: 9.9837 - val_mean_absolute_error: 2.0980
Epoch 20/200
501/501 [==============================] - 2s 4ms/step - loss: 260665.6094 - mean_absolute_error: 30.7038 - val_loss: 9.7313 - val_mean_absolute_error: 2.0722
Epoch 21/200
501/501 [==============================] - 2s 3ms/step - loss: 260649.4219 - mean_absolute_error: 30.6170 - val_loss: 9.5950 - val_mean_absolute_error: 2.0296
Epoch 22/200
501/501 [==============================] - 2s 3ms/step - loss: 260650.6875 - mean_absolute_error: 30.6658 - val_loss: 9.5892 - val_mean_absolute_error: 2.0110
Epoch 23/200
501/501 [==============================] - 2s 3ms/step - loss: 260628.5000 - mean_absolute_error: 30.5442 - val_loss: 9.5662 - val_mean_absolute_error: 2.0093
Epoch 24/200
501/501 [==============================] - 2s 3ms/step - loss: 260634.4375 - mean_absolute_error: 30.6583 - val_loss: 9.5507 - val_mean_absolute_error: 1.9526
Epoch 25/200
501/501 [==============================] - 2s 3ms/step - loss: 260620.0938 - mean_absolute_error: 30.5163 - val_loss: 9.6918 - val_mean_absolute_error: 1.9938
Epoch 26/200
501/501 [==============================] - 2s 4ms/step - loss: 260614.3125 - mean_absolute_error: 30.7110 - val_loss: 10.0643 - val_mean_absolute_error: 1.9689
Epoch 27/200
501/501 [==============================] - 2s 3ms/step - loss: 260596.2188 - mean_absolute_error: 30.5305 - val_loss: 9.9026 - val_mean_absolute_error: 1.9662
Epoch 28/200
501/501 [==============================] - 2s 3ms/step - loss: 260579.6562 - mean_absolute_error: 30.5251 - val_loss: 10.0247 - val_mean_absolute_error: 1.9512
Epoch 29/200
501/501 [==============================] - 2s 4ms/step - loss: 260568.6094 - mean_absolute_error: 30.4260 - val_loss: 10.1755 - val_mean_absolute_error: 1.9687
Epoch 30/200
501/501 [==============================] - 2s 3ms/step - loss: 260547.0938 - mean_absolute_error: 30.5772 - val_loss: 10.2675 - val_mean_absolute_error: 1.9342
Epoch 31/200
501/501 [==============================] - 2s 4ms/step - loss: 260547.6094 - mean_absolute_error: 30.4336 - val_loss: 10.4829 - val_mean_absolute_error: 1.9665
Epoch 32/200
501/501 [==============================] - 2s 3ms/step - loss: 260520.2656 - mean_absolute_error: 30.6396 - val_loss: 11.1896 - val_mean_absolute_error: 1.9746
Epoch 33/200
501/501 [==============================] - 2s 3ms/step - loss: 260518.2812 - mean_absolute_error: 30.4520 - val_loss: 11.1014 - val_mean_absolute_error: 1.9753
Epoch 34/200
501/501 [==============================] - 2s 3ms/step - loss: 260517.1094 - mean_absolute_error: 30.4434 - val_loss: 11.2337 - val_mean_absolute_error: 1.9803
Epoch 35/200
501/501 [==============================] - 2s 3ms/step - loss: 260515.5000 - mean_absolute_error: 30.4075 - val_loss: 11.5291 - val_mean_absolute_error: 2.0131
Epoch 36/200
501/501 [==============================] - 2s 3ms/step - loss: 260513.7812 - mean_absolute_error: 30.4272 - val_loss: 11.7498 - val_mean_absolute_error: 1.9960
Epoch 37/200
501/501 [==============================] - 2s 4ms/step - loss: 260500.3906 - mean_absolute_error: 30.3956 - val_loss: 12.0402 - val_mean_absolute_error: 2.0182
Epoch 38/200
501/501 [==============================] - 2s 4ms/step - loss: 260453.5938 - mean_absolute_error: 30.3968 - val_loss: 12.6304 - val_mean_absolute_error: 2.0411
Epoch 39/200
501/501 [==============================] - 2s 4ms/step - loss: 260449.0469 - mean_absolute_error: 30.4836 - val_loss: 12.6347 - val_mean_absolute_error: 2.0407
Epoch 40/200
501/501 [==============================] - 2s 4ms/step - loss: 260462.2344 - mean_absolute_error: 30.3912 - val_loss: 13.0867 - val_mean_absolute_error: 2.1056
Epoch 41/200
501/501 [==============================] - 2s 3ms/step - loss: 260433.1406 - mean_absolute_error: 30.6067 - val_loss: 13.6718 - val_mean_absolute_error: 2.0818
Epoch 42/200
501/501 [==============================] - 2s 3ms/step - loss: 260426.2188 - mean_absolute_error: 30.4567 - val_loss: 13.4016 - val_mean_absolute_error: 2.0815
Epoch 43/200
501/501 [==============================] - 2s 3ms/step - loss: 260414.0312 - mean_absolute_error: 30.4203 - val_loss: 13.4936 - val_mean_absolute_error: 2.0911
Epoch 44/200
501/501 [==============================] - 2s 3ms/step - loss: 260412.7969 - mean_absolute_error: 30.4120 - val_loss: 13.7332 - val_mean_absolute_error: 2.0947
Epoch 45/200
501/501 [==============================] - 2s 3ms/step - loss: 260395.1875 - mean_absolute_error: 30.3690 - val_loss: 14.1532 - val_mean_absolute_error: 2.1330
Epoch 46/200
501/501 [==============================] - 2s 3ms/step - loss: 260388.3594 - mean_absolute_error: 30.3801 - val_loss: 14.2362 - val_mean_absolute_error: 2.1438
Epoch 47/200
501/501 [==============================] - 2s 4ms/step - loss: 260391.9531 - mean_absolute_error: 30.3772 - val_loss: 14.6372 - val_mean_absolute_error: 2.1821
Epoch 48/200
501/501 [==============================] - 2s 3ms/step - loss: 260377.9688 - mean_absolute_error: 30.4046 - val_loss: 14.9736 - val_mean_absolute_error: 2.1950
Epoch 49/200
501/501 [==============================] - 2s 3ms/step - loss: 260331.8438 - mean_absolute_error: 30.3602 - val_loss: 15.5592 - val_mean_absolute_error: 2.3098
Epoch 50/200
501/501 [==============================] - 2s 3ms/step - loss: 260365.8281 - mean_absolute_error: 30.4893 - val_loss: 15.1093 - val_mean_absolute_error: 2.1826
Epoch 51/200
501/501 [==============================] - 2s 3ms/step - loss: 260341.1094 - mean_absolute_error: 30.3557 - val_loss: 15.7143 - val_mean_absolute_error: 2.2603
Epoch 52/200
501/501 [==============================] - 2s 3ms/step - loss: 260312.3750 - mean_absolute_error: 30.4428 - val_loss: 16.3912 - val_mean_absolute_error: 2.3383
Epoch 53/200
501/501 [==============================] - 2s 3ms/step - loss: 260327.2188 - mean_absolute_error: 30.3505 - val_loss: 16.6313 - val_mean_absolute_error: 2.4071
Epoch 54/200
501/501 [==============================] - 2s 4ms/step - loss: 260346.1250 - mean_absolute_error: 30.5651 - val_loss: 16.4434 - val_mean_absolute_error: 2.2893
Epoch 55/200
501/501 [==============================] - 2s 3ms/step - loss: 260303.2344 - mean_absolute_error: 30.3957 - val_loss: 17.0627 - val_mean_absolute_error: 2.3719
Epoch 56/200
501/501 [==============================] - 2s 4ms/step - loss: 260292.5625 - mean_absolute_error: 30.4464 - val_loss: 17.1917 - val_mean_absolute_error: 2.4154
Epoch 57/200
501/501 [==============================] - 2s 4ms/step - loss: 260288.1406 - mean_absolute_error: 30.3917 - val_loss: 17.1186 - val_mean_absolute_error: 2.4311
Epoch 58/200
501/501 [==============================] - 2s 4ms/step - loss: 260249.1719 - mean_absolute_error: 30.4172 - val_loss: 17.2994 - val_mean_absolute_error: 2.4575
Epoch 59/200
501/501 [==============================] - 2s 3ms/step - loss: 260252.3281 - mean_absolute_error: 30.3762 - val_loss: 17.9726 - val_mean_absolute_error: 2.5454
Epoch 60/200
501/501 [==============================] - 2s 3ms/step - loss: 260242.0000 - mean_absolute_error: 30.5965 - val_loss: 17.9777 - val_mean_absolute_error: 2.4742
Epoch 61/200
501/501 [==============================] - 2s 3ms/step - loss: 260229.9375 - mean_absolute_error: 30.4068 - val_loss: 17.8292 - val_mean_absolute_error: 2.5200
Epoch 62/200
501/501 [==============================] - 2s 3ms/step - loss: 260231.2500 - mean_absolute_error: 30.5646 - val_loss: 18.3615 - val_mean_absolute_error: 2.5890
Epoch 63/200
501/501 [==============================] - 2s 3ms/step - loss: 260216.0000 - mean_absolute_error: 30.4134 - val_loss: 18.9675 - val_mean_absolute_error: 2.7392
Epoch 64/200
501/501 [==============================] - 2s 3ms/step - loss: 260200.4219 - mean_absolute_error: 30.6109 - val_loss: 18.7744 - val_mean_absolute_error: 2.5831
Epoch 65/200
501/501 [==============================] - 2s 3ms/step - loss: 260205.9375 - mean_absolute_error: 30.4512 - val_loss: 19.2810 - val_mean_absolute_error: 2.7272
Epoch 66/200
501/501 [==============================] - 2s 4ms/step - loss: 260178.7656 - mean_absolute_error: 30.6185 - val_loss: 18.3390 - val_mean_absolute_error: 2.5171
Epoch 67/200
501/501 [==============================] - 2s 3ms/step - loss: 260151.2812 - mean_absolute_error: 30.4394 - val_loss: 18.1316 - val_mean_absolute_error: 2.5722
Epoch 68/200
501/501 [==============================] - 2s 3ms/step - loss: 260169.3594 - mean_absolute_error: 30.5418 - val_loss: 19.0179 - val_mean_absolute_error: 2.6203
Epoch 69/200
501/501 [==============================] - 2s 4ms/step - loss: 260153.2031 - mean_absolute_error: 30.4655 - val_loss: 18.5108 - val_mean_absolute_error: 2.6068
Epoch 70/200
501/501 [==============================] - 2s 4ms/step - loss: 260144.4219 - mean_absolute_error: 30.6337 - val_loss: 17.9826 - val_mean_absolute_error: 2.4907
Epoch 71/200
501/501 [==============================] - 2s 3ms/step - loss: 260130.7812 - mean_absolute_error: 30.4464 - val_loss: 17.9979 - val_mean_absolute_error: 2.5546
Epoch 72/200
501/501 [==============================] - 2s 3ms/step - loss: 260107.6562 - mean_absolute_error: 30.5867 - val_loss: 18.8816 - val_mean_absolute_error: 2.5917
Epoch 73/200
501/501 [==============================] - 2s 3ms/step - loss: 260104.1875 - mean_absolute_error: 30.5349 - val_loss: 18.2207 - val_mean_absolute_error: 2.5414
Epoch 74/200
501/501 [==============================] - 2s 3ms/step - loss: 260116.4375 - mean_absolute_error: 30.4866 - val_loss: 17.7991 - val_mean_absolute_error: 2.5463
Epoch 75/200
501/501 [==============================] - 2s 4ms/step - loss: 260085.5781 - mean_absolute_error: 30.4736 - val_loss: 17.4158 - val_mean_absolute_error: 2.5124
Epoch 76/200
501/501 [==============================] - 2s 4ms/step - loss: 260079.8281 - mean_absolute_error: 30.4645 - val_loss: 17.6827 - val_mean_absolute_error: 2.5294
Epoch 77/200
501/501 [==============================] - 2s 4ms/step - loss: 260096.9844 - mean_absolute_error: 30.5143 - val_loss: 18.3939 - val_mean_absolute_error: 2.5757
Epoch 78/200
501/501 [==============================] - 2s 4ms/step - loss: 260059.7031 - mean_absolute_error: 30.5489 - val_loss: 18.6486 - val_mean_absolute_error: 2.5276
Epoch 79/200
501/501 [==============================] - 2s 3ms/step - loss: 260023.4688 - mean_absolute_error: 30.5485 - val_loss: 19.7555 - val_mean_absolute_error: 2.7394
Epoch 80/200
501/501 [==============================] - 2s 3ms/step - loss: 260055.8281 - mean_absolute_error: 30.6277 - val_loss: 18.5964 - val_mean_absolute_error: 2.5982
Epoch 81/200
501/501 [==============================] - 2s 3ms/step - loss: 259997.1875 - mean_absolute_error: 30.5810 - val_loss: 18.0498 - val_mean_absolute_error: 2.5853
Epoch 82/200
501/501 [==============================] - 2s 4ms/step - loss: 260006.6094 - mean_absolute_error: 30.5169 - val_loss: 17.6961 - val_mean_absolute_error: 2.5462
Epoch 83/200
501/501 [==============================] - 2s 3ms/step - loss: 259984.6094 - mean_absolute_error: 30.5403 - val_loss: 16.9435 - val_mean_absolute_error: 2.4562
Epoch 84/200
501/501 [==============================] - 2s 4ms/step - loss: 259989.3906 - mean_absolute_error: 30.4694 - val_loss: 17.2818 - val_mean_absolute_error: 2.5123
Epoch 85/200
501/501 [==============================] - 2s 3ms/step - loss: 259977.3594 - mean_absolute_error: 30.7104 - val_loss: 20.0342 - val_mean_absolute_error: 2.6798
Epoch 86/200
501/501 [==============================] - 2s 3ms/step - loss: 259958.1406 - mean_absolute_error: 30.5556 - val_loss: 18.6603 - val_mean_absolute_error: 2.6184
Epoch 87/200
501/501 [==============================] - 2s 3ms/step - loss: 259955.8281 - mean_absolute_error: 30.5663 - val_loss: 17.5511 - val_mean_absolute_error: 2.5258
Epoch 88/200
501/501 [==============================] - 2s 3ms/step - loss: 259954.2969 - mean_absolute_error: 30.4349 - val_loss: 19.3164 - val_mean_absolute_error: 2.6124
Epoch 89/200
501/501 [==============================] - 2s 3ms/step - loss: 259964.5000 - mean_absolute_error: 30.6754 - val_loss: 21.9235 - val_mean_absolute_error: 2.7842
Epoch 90/200
501/501 [==============================] - 2s 3ms/step - loss: 259929.1562 - mean_absolute_error: 30.5831 - val_loss: 20.9279 - val_mean_absolute_error: 2.7513
Epoch 91/200
501/501 [==============================] - 2s 3ms/step - loss: 259914.6719 - mean_absolute_error: 30.5151 - val_loss: 21.6727 - val_mean_absolute_error: 2.8882
Epoch 92/200
501/501 [==============================] - 2s 3ms/step - loss: 259877.5781 - mean_absolute_error: 30.7906 - val_loss: 21.7458 - val_mean_absolute_error: 2.7605
Epoch 93/200
501/501 [==============================] - 2s 3ms/step - loss: 259916.3750 - mean_absolute_error: 30.5559 - val_loss: 20.2188 - val_mean_absolute_error: 2.7549
Epoch 94/200
501/501 [==============================] - 2s 4ms/step - loss: 259867.3125 - mean_absolute_error: 30.5706 - val_loss: 18.9062 - val_mean_absolute_error: 2.5939
Epoch 95/200
501/501 [==============================] - 2s 3ms/step - loss: 259869.6094 - mean_absolute_error: 30.5363 - val_loss: 18.3573 - val_mean_absolute_error: 2.5687
Epoch 96/200
501/501 [==============================] - 2s 3ms/step - loss: 259853.1562 - mean_absolute_error: 30.4379 - val_loss: 19.5411 - val_mean_absolute_error: 2.6719
Epoch 97/200
501/501 [==============================] - 2s 3ms/step - loss: 259869.5156 - mean_absolute_error: 31.0359 - val_loss: 21.8523 - val_mean_absolute_error: 2.7039
Epoch 98/200
501/501 [==============================] - 2s 4ms/step - loss: 259830.4062 - mean_absolute_error: 30.6326 - val_loss: 18.5637 - val_mean_absolute_error: 2.5752
Epoch 99/200
501/501 [==============================] - 2s 3ms/step - loss: 259833.2812 - mean_absolute_error: 30.6987 - val_loss: 20.3129 - val_mean_absolute_error: 2.6726
Epoch 100/200
501/501 [==============================] - 2s 4ms/step - loss: 259864.4062 - mean_absolute_error: 30.5071 - val_loss: 20.9459 - val_mean_absolute_error: 2.6710
Epoch 101/200
501/501 [==============================] - 2s 3ms/step - loss: 259850.7812 - mean_absolute_error: 30.7576 - val_loss: 23.7439 - val_mean_absolute_error: 2.8832
Epoch 102/200
501/501 [==============================] - 2s 5ms/step - loss: 259801.3125 - mean_absolute_error: 30.6810 - val_loss: 22.6964 - val_mean_absolute_error: 2.8528
Epoch 103/200
501/501 [==============================] - 2s 4ms/step - loss: 259815.9531 - mean_absolute_error: 30.6733 - val_loss: 21.5569 - val_mean_absolute_error: 2.7685
Epoch 104/200
501/501 [==============================] - 2s 4ms/step - loss: 259771.1875 - mean_absolute_error: 30.5228 - val_loss: 20.9442 - val_mean_absolute_error: 2.7685
Epoch 105/200
501/501 [==============================] - 2s 4ms/step - loss: 259804.8438 - mean_absolute_error: 30.7632 - val_loss: 19.4815 - val_mean_absolute_error: 2.5429
Epoch 106/200
501/501 [==============================] - 2s 4ms/step - loss: 259756.6406 - mean_absolute_error: 30.4881 - val_loss: 20.1929 - val_mean_absolute_error: 2.5904
Epoch 107/200
501/501 [==============================] - 2s 3ms/step - loss: 259798.0625 - mean_absolute_error: 30.6754 - val_loss: 22.1621 - val_mean_absolute_error: 2.7479
Epoch 108/200
501/501 [==============================] - 2s 3ms/step - loss: 259765.7188 - mean_absolute_error: 30.4904 - val_loss: 20.4480 - val_mean_absolute_error: 2.6466
Epoch 109/200
501/501 [==============================] - 2s 3ms/step - loss: 259729.3594 - mean_absolute_error: 30.7016 - val_loss: 19.5665 - val_mean_absolute_error: 2.5376
Epoch 110/200
501/501 [==============================] - 2s 3ms/step - loss: 259728.5469 - mean_absolute_error: 30.3984 - val_loss: 21.0117 - val_mean_absolute_error: 2.5817
Epoch 111/200
501/501 [==============================] - 2s 4ms/step - loss: 259758.5938 - mean_absolute_error: 30.7618 - val_loss: 25.2178 - val_mean_absolute_error: 2.8680
Epoch 112/200
501/501 [==============================] - 2s 4ms/step - loss: 259695.0156 - mean_absolute_error: 30.7486 - val_loss: 24.8810 - val_mean_absolute_error: 2.8505
Epoch 113/200
501/501 [==============================] - 2s 4ms/step - loss: 259688.9688 - mean_absolute_error: 30.6240 - val_loss: 24.4025 - val_mean_absolute_error: 2.8617
Epoch 114/200
501/501 [==============================] - 2s 4ms/step - loss: 259624.7188 - mean_absolute_error: 30.6618 - val_loss: 23.1586 - val_mean_absolute_error: 2.7545
Epoch 115/200
501/501 [==============================] - 2s 3ms/step - loss: 259639.0469 - mean_absolute_error: 30.4829 - val_loss: 20.8940 - val_mean_absolute_error: 2.6464
Epoch 116/200
501/501 [==============================] - 2s 4ms/step - loss: 259632.0312 - mean_absolute_error: 30.5878 - val_loss: 20.2923 - val_mean_absolute_error: 2.5308
Epoch 117/200
501/501 [==============================] - 2s 3ms/step - loss: 259704.8750 - mean_absolute_error: 30.4808 - val_loss: 20.3472 - val_mean_absolute_error: 2.5639
Epoch 118/200
501/501 [==============================] - 2s 4ms/step - loss: 259612.9688 - mean_absolute_error: 30.8324 - val_loss: 24.5936 - val_mean_absolute_error: 2.8241
Epoch 119/200
501/501 [==============================] - 2s 3ms/step - loss: 259640.3750 - mean_absolute_error: 30.6095 - val_loss: 21.4986 - val_mean_absolute_error: 2.7195
Epoch 120/200
501/501 [==============================] - 2s 4ms/step - loss: 259647.1094 - mean_absolute_error: 30.7547 - val_loss: 20.0624 - val_mean_absolute_error: 2.5151
Epoch 121/200
501/501 [==============================] - 2s 3ms/step - loss: 259618.8594 - mean_absolute_error: 30.6892 - val_loss: 20.8285 - val_mean_absolute_error: 2.5938
Epoch 122/200
501/501 [==============================] - 2s 4ms/step - loss: 259615.2656 - mean_absolute_error: 30.5285 - val_loss: 20.1657 - val_mean_absolute_error: 2.5588
Epoch 123/200
501/501 [==============================] - 2s 4ms/step - loss: 259617.1719 - mean_absolute_error: 30.9343 - val_loss: 21.5952 - val_mean_absolute_error: 2.6187
Epoch 124/200
501/501 [==============================] - 2s 4ms/step - loss: 259573.6406 - mean_absolute_error: 30.5889 - val_loss: 20.4744 - val_mean_absolute_error: 2.5195
Epoch 125/200
501/501 [==============================] - 2s 3ms/step - loss: 259539.1250 - mean_absolute_error: 30.5513 - val_loss: 20.2873 - val_mean_absolute_error: 2.5218
Epoch 126/200
501/501 [==============================] - 2s 3ms/step - loss: 259584.4531 - mean_absolute_error: 30.4283 - val_loss: 22.1396 - val_mean_absolute_error: 2.6143
Epoch 127/200
501/501 [==============================] - 2s 3ms/step - loss: 259561.4531 - mean_absolute_error: 30.7230 - val_loss: 20.7738 - val_mean_absolute_error: 2.5848
Epoch 128/200
501/501 [==============================] - 2s 3ms/step - loss: 259558.4375 - mean_absolute_error: 30.4008 - val_loss: 20.1169 - val_mean_absolute_error: 2.5137
Epoch 129/200
501/501 [==============================] - 2s 4ms/step - loss: 259508.8906 - mean_absolute_error: 30.5457 - val_loss: 21.4751 - val_mean_absolute_error: 2.5580
Epoch 130/200
501/501 [==============================] - 2s 3ms/step - loss: 259545.4062 - mean_absolute_error: 30.4502 - val_loss: 22.3325 - val_mean_absolute_error: 2.6661
Epoch 131/200
501/501 [==============================] - 2s 3ms/step - loss: 259558.7344 - mean_absolute_error: 30.6563 - val_loss: 24.4177 - val_mean_absolute_error: 2.6372
Epoch 132/200
501/501 [==============================] - 2s 3ms/step - loss: 259522.8281 - mean_absolute_error: 31.1888 - val_loss: 29.7138 - val_mean_absolute_error: 2.9662
Epoch 133/200
501/501 [==============================] - 2s 3ms/step - loss: 259521.6875 - mean_absolute_error: 30.8536 - val_loss: 26.2694 - val_mean_absolute_error: 2.8562
Epoch 134/200
501/501 [==============================] - 2s 3ms/step - loss: 259508.5625 - mean_absolute_error: 30.7515 - val_loss: 23.8000 - val_mean_absolute_error: 2.6932
Epoch 135/200
501/501 [==============================] - 2s 3ms/step - loss: 259459.2188 - mean_absolute_error: 30.6157 - val_loss: 21.5014 - val_mean_absolute_error: 2.5659
Epoch 136/200
501/501 [==============================] - 2s 3ms/step - loss: 259441.2188 - mean_absolute_error: 30.4868 - val_loss: 23.7934 - val_mean_absolute_error: 2.6463
Epoch 137/200
501/501 [==============================] - 2s 3ms/step - loss: 259413.3594 - mean_absolute_error: 30.5451 - val_loss: 23.9045 - val_mean_absolute_error: 2.7162
Epoch 138/200
501/501 [==============================] - 2s 4ms/step - loss: 259437.3750 - mean_absolute_error: 30.5040 - val_loss: 24.4658 - val_mean_absolute_error: 2.6835
Epoch 139/200
501/501 [==============================] - 2s 4ms/step - loss: 259452.9688 - mean_absolute_error: 30.5354 - val_loss: 23.3943 - val_mean_absolute_error: 2.6421
Epoch 140/200
501/501 [==============================] - 2s 3ms/step - loss: 259454.8438 - mean_absolute_error: 30.4069 - val_loss: 22.8800 - val_mean_absolute_error: 2.6393
Epoch 141/200
501/501 [==============================] - 2s 4ms/step - loss: 259416.1719 - mean_absolute_error: 30.5802 - val_loss: 22.7050 - val_mean_absolute_error: 2.5803
Epoch 142/200
501/501 [==============================] - 2s 3ms/step - loss: 259395.4844 - mean_absolute_error: 30.3961 - val_loss: 22.1188 - val_mean_absolute_error: 2.5822
Epoch 143/200
501/501 [==============================] - 2s 3ms/step - loss: 259347.9531 - mean_absolute_error: 30.5907 - val_loss: 24.1933 - val_mean_absolute_error: 2.6889
Epoch 144/200
501/501 [==============================] - 2s 3ms/step - loss: 259344.9219 - mean_absolute_error: 30.4284 - val_loss: 25.0915 - val_mean_absolute_error: 2.6487
Epoch 145/200
501/501 [==============================] - 2s 3ms/step - loss: 259321.4844 - mean_absolute_error: 30.5033 - val_loss: 26.3385 - val_mean_absolute_error: 2.7767
Epoch 146/200
501/501 [==============================] - 2s 3ms/step - loss: 259358.1250 - mean_absolute_error: 30.5348 - val_loss: 24.6696 - val_mean_absolute_error: 2.6135
Epoch 147/200
501/501 [==============================] - 2s 4ms/step - loss: 259309.6562 - mean_absolute_error: 30.8699 - val_loss: 31.9552 - val_mean_absolute_error: 3.0397
Epoch 148/200
501/501 [==============================] - 2s 3ms/step - loss: 259392.0625 - mean_absolute_error: 30.6235 - val_loss: 30.1417 - val_mean_absolute_error: 2.9569
Epoch 149/200
501/501 [==============================] - 2s 3ms/step - loss: 259282.7969 - mean_absolute_error: 30.9127 - val_loss: 28.7623 - val_mean_absolute_error: 2.9123
Epoch 150/200
501/501 [==============================] - 2s 3ms/step - loss: 259283.9844 - mean_absolute_error: 30.5673 - val_loss: 26.6851 - val_mean_absolute_error: 2.8187
Epoch 151/200
501/501 [==============================] - 2s 3ms/step - loss: 259290.5625 - mean_absolute_error: 30.6770 - val_loss: 24.2699 - val_mean_absolute_error: 2.6346
Epoch 152/200
501/501 [==============================] - 2s 3ms/step - loss: 259347.5469 - mean_absolute_error: 30.4721 - val_loss: 26.7382 - val_mean_absolute_error: 2.6776
Epoch 153/200
501/501 [==============================] - 2s 3ms/step - loss: 259298.9531 - mean_absolute_error: 30.7308 - val_loss: 30.8590 - val_mean_absolute_error: 2.9444
Epoch 154/200
501/501 [==============================] - 2s 3ms/step - loss: 259271.6250 - mean_absolute_error: 30.5378 - val_loss: 29.6793 - val_mean_absolute_error: 2.9056
Epoch 155/200
501/501 [==============================] - 2s 3ms/step - loss: 259244.1094 - mean_absolute_error: 30.6281 - val_loss: 27.9609 - val_mean_absolute_error: 2.7700
Epoch 156/200
501/501 [==============================] - 2s 3ms/step - loss: 259328.1719 - mean_absolute_error: 30.4736 - val_loss: 26.9987 - val_mean_absolute_error: 2.7148
Epoch 157/200
501/501 [==============================] - 2s 4ms/step - loss: 259284.1719 - mean_absolute_error: 30.5951 - val_loss: 27.5849 - val_mean_absolute_error: 2.7902
Epoch 158/200
501/501 [==============================] - 2s 3ms/step - loss: 259200.3438 - mean_absolute_error: 30.4329 - val_loss: 27.1120 - val_mean_absolute_error: 2.7171
Epoch 159/200
501/501 [==============================] - 2s 3ms/step - loss: 259217.9844 - mean_absolute_error: 30.8414 - val_loss: 29.6938 - val_mean_absolute_error: 2.8192
Epoch 160/200
501/501 [==============================] - 2s 3ms/step - loss: 259217.2969 - mean_absolute_error: 30.4625 - val_loss: 24.9298 - val_mean_absolute_error: 2.6503
Epoch 161/200
501/501 [==============================] - 2s 3ms/step - loss: 259178.1250 - mean_absolute_error: 30.6962 - val_loss: 25.4819 - val_mean_absolute_error: 2.6244
Epoch 162/200
501/501 [==============================] - 2s 3ms/step - loss: 259178.0625 - mean_absolute_error: 30.4821 - val_loss: 22.9266 - val_mean_absolute_error: 2.5262
Epoch 163/200
501/501 [==============================] - 2s 3ms/step - loss: 259161.9375 - mean_absolute_error: 30.3549 - val_loss: 24.1002 - val_mean_absolute_error: 2.6027
Epoch 164/200
501/501 [==============================] - 2s 3ms/step - loss: 259120.5625 - mean_absolute_error: 30.4824 - val_loss: 24.6062 - val_mean_absolute_error: 2.6291
Epoch 165/200
501/501 [==============================] - 2s 4ms/step - loss: 259160.1094 - mean_absolute_error: 30.3915 - val_loss: 25.1952 - val_mean_absolute_error: 2.6627
Epoch 166/200
501/501 [==============================] - 2s 4ms/step - loss: 259125.2969 - mean_absolute_error: 31.0181 - val_loss: 32.4063 - val_mean_absolute_error: 3.0310
Epoch 167/200
501/501 [==============================] - 2s 3ms/step - loss: 259197.8438 - mean_absolute_error: 30.6454 - val_loss: 28.8840 - val_mean_absolute_error: 2.8560
Epoch 168/200
501/501 [==============================] - 2s 4ms/step - loss: 259121.3438 - mean_absolute_error: 30.7387 - val_loss: 28.9965 - val_mean_absolute_error: 2.9243
Epoch 169/200
501/501 [==============================] - 2s 4ms/step - loss: 259109.4219 - mean_absolute_error: 30.5971 - val_loss: 26.4365 - val_mean_absolute_error: 2.8056
Epoch 170/200
501/501 [==============================] - 2s 4ms/step - loss: 259044.4375 - mean_absolute_error: 30.5890 - val_loss: 26.3343 - val_mean_absolute_error: 2.7405
Epoch 171/200
501/501 [==============================] - 2s 3ms/step - loss: 259100.3594 - mean_absolute_error: 30.5155 - val_loss: 24.9448 - val_mean_absolute_error: 2.7181
Epoch 172/200
501/501 [==============================] - 2s 3ms/step - loss: 259064.5000 - mean_absolute_error: 30.6616 - val_loss: 25.0858 - val_mean_absolute_error: 2.6478
Epoch 173/200
501/501 [==============================] - 2s 3ms/step - loss: 259062.2031 - mean_absolute_error: 30.4758 - val_loss: 24.5966 - val_mean_absolute_error: 2.6542
Epoch 174/200
501/501 [==============================] - 2s 4ms/step - loss: 259078.3906 - mean_absolute_error: 30.4178 - val_loss: 24.4360 - val_mean_absolute_error: 2.5819
Epoch 175/200
501/501 [==============================] - 2s 4ms/step - loss: 259060.1250 - mean_absolute_error: 30.5107 - val_loss: 28.4264 - val_mean_absolute_error: 2.7729
Epoch 176/200
501/501 [==============================] - 2s 4ms/step - loss: 259035.6250 - mean_absolute_error: 30.4872 - val_loss: 26.7907 - val_mean_absolute_error: 2.7030
Epoch 177/200
501/501 [==============================] - 2s 4ms/step - loss: 258983.5625 - mean_absolute_error: 30.7168 - val_loss: 29.4184 - val_mean_absolute_error: 2.8804
Epoch 178/200
501/501 [==============================] - 2s 3ms/step - loss: 259099.0000 - mean_absolute_error: 30.4981 - val_loss: 30.2047 - val_mean_absolute_error: 2.8527
Epoch 179/200
501/501 [==============================] - 2s 3ms/step - loss: 259014.2969 - mean_absolute_error: 30.6986 - val_loss: 34.4102 - val_mean_absolute_error: 2.9292
Epoch 180/200
501/501 [==============================] - 2s 3ms/step - loss: 259009.9688 - mean_absolute_error: 30.6176 - val_loss: 31.5209 - val_mean_absolute_error: 2.7934
Epoch 181/200
501/501 [==============================] - 2s 3ms/step - loss: 258972.0781 - mean_absolute_error: 30.4706 - val_loss: 28.6259 - val_mean_absolute_error: 2.6865
Epoch 182/200
501/501 [==============================] - 2s 4ms/step - loss: 258985.3594 - mean_absolute_error: 30.5693 - val_loss: 26.9487 - val_mean_absolute_error: 2.6082
Epoch 183/200
501/501 [==============================] - 2s 3ms/step - loss: 258983.4688 - mean_absolute_error: 30.7164 - val_loss: 34.4562 - val_mean_absolute_error: 2.9783
Epoch 184/200
501/501 [==============================] - 2s 4ms/step - loss: 258976.1562 - mean_absolute_error: 30.5661 - val_loss: 32.6643 - val_mean_absolute_error: 2.9231
Epoch 185/200
501/501 [==============================] - 2s 4ms/step - loss: 258928.2188 - mean_absolute_error: 30.5372 - val_loss: 30.1939 - val_mean_absolute_error: 2.8019
Epoch 186/200
501/501 [==============================] - 2s 3ms/step - loss: 258952.5625 - mean_absolute_error: 30.3758 - val_loss: 31.0606 - val_mean_absolute_error: 2.8305
Epoch 187/200
501/501 [==============================] - 2s 3ms/step - loss: 258903.6250 - mean_absolute_error: 30.7325 - val_loss: 34.5546 - val_mean_absolute_error: 2.9765
Epoch 188/200
501/501 [==============================] - 2s 4ms/step - loss: 258958.0469 - mean_absolute_error: 30.8061 - val_loss: 34.9509 - val_mean_absolute_error: 3.0035
Epoch 189/200
501/501 [==============================] - 2s 3ms/step - loss: 258900.6562 - mean_absolute_error: 30.5755 - val_loss: 30.1790 - val_mean_absolute_error: 2.8439
Epoch 190/200
501/501 [==============================] - 2s 3ms/step - loss: 258893.7656 - mean_absolute_error: 30.5135 - val_loss: 31.6960 - val_mean_absolute_error: 2.9175
Epoch 191/200
501/501 [==============================] - 2s 3ms/step - loss: 258925.9688 - mean_absolute_error: 30.3642 - val_loss: 30.1893 - val_mean_absolute_error: 2.8385
Epoch 192/200
501/501 [==============================] - 2s 3ms/step - loss: 258826.3438 - mean_absolute_error: 30.4609 - val_loss: 27.8942 - val_mean_absolute_error: 2.7800
Epoch 193/200
501/501 [==============================] - 2s 3ms/step - loss: 258883.2188 - mean_absolute_error: 30.4673 - val_loss: 29.7278 - val_mean_absolute_error: 2.7349
Epoch 194/200
501/501 [==============================] - 2s 4ms/step - loss: 258848.0781 - mean_absolute_error: 30.4030 - val_loss: 28.3689 - val_mean_absolute_error: 2.7899
Epoch 195/200
501/501 [==============================] - 2s 3ms/step - loss: 258801.3906 - mean_absolute_error: 30.6590 - val_loss: 28.4875 - val_mean_absolute_error: 2.5997
Epoch 196/200
501/501 [==============================] - 2s 3ms/step - loss: 258807.5156 - mean_absolute_error: 30.4316 - val_loss: 27.3241 - val_mean_absolute_error: 2.6365
Epoch 197/200
501/501 [==============================] - 2s 3ms/step - loss: 258821.0469 - mean_absolute_error: 30.4334 - val_loss: 34.4295 - val_mean_absolute_error: 2.9534
Epoch 198/200
501/501 [==============================] - 2s 3ms/step - loss: 258763.4062 - mean_absolute_error: 30.8836 - val_loss: 38.9884 - val_mean_absolute_error: 3.1819
Epoch 199/200
501/501 [==============================] - 2s 3ms/step - loss: 258774.4844 - mean_absolute_error: 30.8386 - val_loss: 29.5166 - val_mean_absolute_error: 2.7986
Epoch 200/200
501/501 [==============================] - 2s 3ms/step - loss: 258843.1406 - mean_absolute_error: 30.4756 - val_loss: 30.0912 - val_mean_absolute_error: 2.8136


from matplotlib import pylab as plt


plt.plot(history.history['loss'],label='train')
plt.plot(history.history['val_loss'],label='test')
plt.legend()

<matplotlib.legend.Legend at 0x1897a192388>


x_test_reshaped=x_test_scaled.reshape((x_test_scaled.shape[0],1,x_test_scaled.shape[1]))


pred = model.predict(x_test_reshaped)


pred.shape

(1464, 3)


test_data['power_pred']=pred

C:\Users\suraj\anaconda3\lib\site-packages\ipykernel_launcher.py:1: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy


test_data.head()


power_series=test_data.loc[:,('power','power_pred','power_pred1')]
power_series.plot()

<AxesSubplot:xlabel='datetime'>


fig=px.line(test_data,y=['power_pred','power','power_pred1'],title='Energy generation')

fig.update_xaxes(
    rangeslider_visible= True,
    rangeselector=dict(
                        buttons = list([
                        dict(count = 1,label = '1m',step='month',stepmode = "backward"),
                        dict(count = 2,label = '6m',step='month',stepmode = "backward"),
                        dict(count = 3,label = '6mYTD',step='month',stepmode = "todate"),
                        dict(count = 4,label = '2y',step='year',stepmode = "backward"),
                        dict(count = 5,label = '3y',step='year',stepmode = "backward"),
                        dict(step= 'all')
                            ])        
                        )
                   )
fig.show()


fig=px.line(test_data,y=['power','power_pred1'],title='Energy generation')

fig.update_xaxes(
    rangeslider_visible= True,
    rangeselector=dict(
                        buttons = list([
                        dict(count = 1,label = '1m',step='month',stepmode = "backward"),
                        dict(count = 2,label = '6m',step='month',stepmode = "backward"),
                        dict(count = 3,label = '6mYTD',step='month',stepmode = "todate"),
                        dict(count = 4,label = '2y',step='year',stepmode = "backward"),
                        dict(count = 5,label = '3y',step='year',stepmode = "backward"),
                        dict(step= 'all')
                            ])        
                        )
                   )
fig.show()


new_test=df_weather_forecast.loc['2019-10-01':'2019-10-27']


x_test_n, y_test_n=new_test.drop(['power'],axis=1),new_test.power


x_new_test_scaled=scaler.fit_transform(x_test_n)


x_new_test_reshaped=x_new_test_scaled.reshape((x_new_test_scaled.shape[0],1,x_new_test_scaled.shape[1]))


x_test_reshaped.shape

(1464, 1, 17)


preds = model.predict(x_new_test_reshaped)


new_test['power_pred']=preds

C:\Users\suraj\anaconda3\lib\site-packages\ipykernel_launcher.py:1: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy


power_series=new_test.loc[:,'power_pred']
power_series.plot()

<AxesSubplot:xlabel='datetime_local'>


fig=px.line(new_test,y='power_pred',title='Energy generation')

fig.update_xaxes(
    rangeslider_visible= True,
    rangeselector=dict(
                        buttons = list([
                        dict(count = 1,label = '1m',step='month',stepmode = "backward"),
                        dict(count = 2,label = '6m',step='month',stepmode = "backward"),
                        dict(count = 3,label = '6mYTD',step='month',stepmode = "todate"),
                        dict(count = 4,label = '2y',step='year',stepmode = "backward"),
                        dict(count = 5,label = '3y',step='year',stepmode = "backward"),
                        dict(step= 'all')
                            ])        
                        )
                   )
fig.show()


new_test.to_csv('forecast_October_2019.csv')

	power
datetime
2017-10-01 00:00:00	0.0000
2017-10-01 01:00:00	0.0000
2017-10-01 02:00:00	0.0000
2017-10-01 03:00:00	0.0000
2017-10-01 04:00:00	0.0000
2017-10-01 05:00:00	0.0000
2017-10-01 06:00:00	0.0200
2017-10-01 07:00:00	1.1925
2017-10-01 08:00:00	5.1375
2017-10-01 09:00:00	8.2250

	Unnamed: 0	plant_id	cloud_cover	apparent_temperature	temperature	humidity	dew_point	wind_bearing	wind_speed	wind_chill	...	qpf	uv_index	snow	pop	fctcode	ozone	precip_accumulation	precip_intensity	precip_probability	visibility
Unnamed: 0	1.000000	NaN	0.214831	0.506807	0.459176	-0.082972	0.245485	0.365803	0.336288	NaN	...	NaN	0.739429	NaN	NaN	NaN	0.739716	NaN	0.739380	0.739377	0.096399
plant_id	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
cloud_cover	0.214831	NaN	1.000000	0.166379	0.164960	-0.071690	0.062932	0.070571	0.075671	NaN	...	NaN	0.320889	NaN	NaN	NaN	0.320888	NaN	0.320889	0.320889	-0.018911
apparent_temperature	0.506807	NaN	0.166379	1.000000	0.969744	-0.181759	0.567757	0.220673	0.187573	NaN	...	NaN	0.385864	NaN	NaN	NaN	0.386468	NaN	0.385658	0.385654	0.028750
temperature	0.459176	NaN	0.164960	0.969744	1.000000	-0.324518	0.432929	0.223806	0.186522	NaN	...	NaN	0.368620	NaN	NaN	NaN	0.369282	NaN	0.368406	0.368404	0.014180
humidity	-0.082972	NaN	-0.071690	-0.181759	-0.324518	1.000000	0.497798	-0.219560	-0.191230	NaN	...	NaN	-0.185654	NaN	NaN	NaN	-0.185402	NaN	-0.185555	-0.185561	0.053018
dew_point	0.245485	NaN	0.062932	0.567757	0.432929	0.497798	1.000000	-0.005577	-0.005434	NaN	...	NaN	0.085216	NaN	NaN	NaN	0.085929	NaN	0.085163	0.085154	0.046960
wind_bearing	0.365803	NaN	0.070571	0.220673	0.223806	-0.219560	-0.005577	1.000000	0.890365	NaN	...	NaN	0.471057	NaN	NaN	NaN	0.471057	NaN	0.471054	0.471054	-0.031508
wind_speed	0.336288	NaN	0.075671	0.187573	0.186522	-0.191230	-0.005434	0.890365	1.000000	NaN	...	NaN	0.427106	NaN	NaN	NaN	0.427104	NaN	0.427106	0.427106	-0.025176
wind_chill	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
wind_gust	0.739460	NaN	0.320889	0.385751	0.368493	-0.185495	0.085277	0.471055	0.427106	NaN	...	NaN	1.000000	NaN	NaN	NaN	0.999996	NaN	1.000000	1.000000	-0.059133
heat_index	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
pressure	0.570359	NaN	0.401547	0.281708	0.264619	-0.124952	0.069054	0.455247	0.476385	NaN	...	NaN	0.772193	NaN	NaN	NaN	0.772187	NaN	0.772193	0.772193	-0.045748
qpf	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
uv_index	0.739429	NaN	0.320889	0.385864	0.368620	-0.185654	0.085216	0.471057	0.427106	NaN	...	NaN	1.000000	NaN	NaN	NaN	0.999995	NaN	1.000000	1.000000	-0.059144
snow	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
pop	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
fctcode	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
ozone	0.739716	NaN	0.320888	0.386468	0.369282	-0.185402	0.085929	0.471057	0.427104	NaN	...	NaN	0.999995	NaN	NaN	NaN	1.000000	NaN	0.999995	0.999995	-0.059745
precip_accumulation	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
precip_intensity	0.739380	NaN	0.320889	0.385658	0.368406	-0.185555	0.085163	0.471054	0.427106	NaN	...	NaN	1.000000	NaN	NaN	NaN	0.999995	NaN	1.000000	1.000000	-0.059145
precip_probability	0.739377	NaN	0.320889	0.385654	0.368404	-0.185561	0.085154	0.471054	0.427106	NaN	...	NaN	1.000000	NaN	NaN	NaN	0.999995	NaN	1.000000	1.000000	-0.059146
visibility	0.096399	NaN	-0.018911	0.028750	0.014180	0.053018	0.046960	-0.031508	-0.025176	NaN	...	NaN	-0.059144	NaN	NaN	NaN	-0.059745	NaN	-0.059145	-0.059146	1.000000

	cloud_cover	apparent_temperature	temperature	humidity	dew_point	wind_bearing	wind_speed	wind_chill	wind_gust	heat_index	...	qpf	uv_index	snow	pop	fctcode	ozone	precip_accumulation	precip_intensity	precip_probability	visibility
count	13619.000000	13619.000000	13619.000000	13619.000000	13619.000000	13619.000000	13619.000000	3744.0	13619.000000	3744.0	...	3744.0	13619.000000	3744.0	3744.0	3744.0	13619.000000	3744.0	13619.000000	13619.000000	13619.000000
mean	-314.005310	25.944639	24.498054	0.537411	11.924626	-498.677363	-540.808704	-9999.0	-2393.184251	-9999.0	...	-9999.0	-2394.989280	-9999.0	-9999.0	-9999.0	-2187.730797	-9999.0	-2396.367775	-2396.398081	-117.086149
std	1744.610028	10.469808	8.840016	0.498617	9.129459	2557.943872	2267.247300	0.0	4270.333680	0.0	...	0.0	4269.320576	0.0	0.0	0.0	4385.706548	0.0	4268.545854	4268.528833	1130.763316
min	-9999.000000	3.140000	5.000000	0.000000	-20.560000	-9999.000000	-9999.000000	-9999.0	-9999.000000	-9999.0	...	-9999.0	-9999.000000	-9999.0	-9999.0	-9999.0	-9999.000000	-9999.0	-9999.000000	-9999.000000	-9999.000000
25%	0.000000	17.310000	17.000000	0.000000	5.920000	79.000000	1.620000	-9999.0	1.185000	-9999.0	...	-9999.0	0.000000	-9999.0	-9999.0	-9999.0	236.900000	-9999.0	0.000000	0.000000	3.540000
50%	0.000000	25.540000	25.000000	1.000000	10.710000	149.000000	2.410000	-9999.0	3.070000	-9999.0	...	-9999.0	0.000000	-9999.0	-9999.0	-9999.0	270.800000	-9999.0	0.000000	0.000000	16.090000
75%	0.410000	34.580000	31.000000	1.000000	19.670000	293.000000	3.330000	-9999.0	4.710000	-9999.0	...	-9999.0	2.000000	-9999.0	-9999.0	-9999.0	280.800000	-9999.0	0.000000	0.000000	16.090000
max	1.000000	49.750000	47.000000	1.000000	28.350000	359.000000	9.730000	-9999.0	15.150000	-9999.0	...	-9999.0	12.000000	-9999.0	-9999.0	-9999.0	351.700000	-9999.0	6.220000	0.730000	16.090000

	cloud_cover	apparent_temperature	temperature	humidity	dew_point	wind_bearing	wind_speed	wind_chill	wind_gust	heat_index	...	ozone	precip_accumulation	precip_intensity	precip_probability	precip_type	visibility	sunrise	sunset	icon	summary
datetime_local
2017-10-01 00:00:00	0.0	26.52	26	1	19.46	308	0.83	-9999.0	-9999.0	-9999.0	...	-9999.0	-9999.0	-9999.0	-9999.0	0.451355	3.57	2017-10-01 00:52:02	2017-10-01 12:45:07	0.225788	0.408767
2017-10-01 01:00:00	0.0	28.78	27	1	20.06	-9999	-9999.00	-9999.0	-9999.0	-9999.0	...	-9999.0	-9999.0	-9999.0	-9999.0	0.451355	3.06	2017-10-01 00:52:02	2017-10-01 12:45:07	0.214406	0.214259
2017-10-01 02:00:00	0.0	26.58	26	1	19.97	-9999	-9999.00	-9999.0	-9999.0	-9999.0	...	-9999.0	-9999.0	-9999.0	-9999.0	0.451355	3.06	2017-10-01 00:52:02	2017-10-01 12:45:07	0.214406	0.214259
2017-10-01 03:00:00	0.0	23.84	23	1	18.66	270	0.45	-9999.0	-9999.0	-9999.0	...	-9999.0	-9999.0	-9999.0	-9999.0	0.451355	3.56	2017-10-01 00:52:02	2017-10-01 12:45:07	0.225788	0.408767
2017-10-01 04:00:00	0.0	24.13	24	1	17.84	-9999	-9999.00	-9999.0	-9999.0	-9999.0	...	-9999.0	-9999.0	-9999.0	-9999.0	0.451355	3.06	2017-10-01 00:52:02	2017-10-01 12:45:07	0.214406	0.214259

	cloud_cover	apparent_temperature	temperature	humidity	dew_point	wind_bearing	wind_speed	wind_chill	wind_gust	heat_index	...	pop	fctcode	ozone	precip_accumulation	precip_intensity	precip_probability	precip_type	visibility	icon	summary
count	13619.000000	13619.000000	13619.000000	13619.000000	13619.000000	13619.000000	13619.000000	3744.0	13619.000000	3744.0	...	3744.0	3744.0	13619.000000	3744.0	13619.000000	13619.000000	6490.000000	13619.000000	13619.000000	13619.000000
mean	-314.005310	25.944639	24.498054	0.537411	11.924626	-498.677363	-540.808704	-9999.0	-2393.184251	-9999.0	...	-9999.0	-9999.0	-2187.730797	-9999.0	-2396.367775	-2396.398081	0.428831	-117.086149	0.182920	0.234222
std	1744.610028	10.469808	8.840016	0.498617	9.129459	2557.943872	2267.247300	0.0	4270.333680	0.0	...	0.0	0.0	4385.706548	0.0	4268.545854	4268.528833	0.095356	1130.763316	0.053470	0.156872
min	-9999.000000	3.140000	5.000000	0.000000	-20.560000	-9999.000000	-9999.000000	-9999.0	-9999.000000	-9999.0	...	-9999.0	-9999.0	-9999.000000	-9999.0	-9999.000000	-9999.000000	0.025185	-9999.000000	0.000147	0.000073
25%	0.000000	17.310000	17.000000	0.000000	5.920000	79.000000	1.620000	-9999.0	1.185000	-9999.0	...	-9999.0	-9999.0	236.900000	-9999.0	0.000000	0.000000	0.451355	3.540000	0.129819	0.060651
50%	0.000000	25.540000	25.000000	1.000000	10.710000	149.000000	2.410000	-9999.0	3.070000	-9999.0	...	-9999.0	-9999.0	270.800000	-9999.0	0.000000	0.000000	0.451355	16.090000	0.214406	0.214259
75%	0.410000	34.580000	31.000000	1.000000	19.670000	293.000000	3.330000	-9999.0	4.710000	-9999.0	...	-9999.0	-9999.0	280.800000	-9999.0	0.000000	0.000000	0.451355	16.090000	0.220060	0.408767
max	1.000000	49.750000	47.000000	1.000000	28.350000	359.000000	9.730000	-9999.0	15.150000	-9999.0	...	-9999.0	-9999.0	351.700000	-9999.0	6.220000	0.730000	0.451355	16.090000	0.225788	0.408767

Data Preparation¶

Creating date and time field¶

EDA¶

Filling missing dates data with moving average¶

Feature Engineering¶

Saving Final Cleaned Weather Data¶

Preprocessing the Test Data¶

Feature Engineering¶

Train Test Split¶

Feature Scaling¶

LSTM Modelling¶

Test Forecasting for the Month of October¶

	Unnamed: 0	ghi	gti	power
count	70080.000000	70080.0	70080.0	70080.000000
mean	35039.500000	0.0	0.0	28.612915
std	20230.497769	0.0	0.0	498.795887
min	0.000000	0.0	0.0	0.000000
25%	17519.750000	0.0	0.0	0.000000
50%	35039.500000	0.0	0.0	0.000000
75%	52559.250000	0.0	0.0	5.540000
max	70079.000000	0.0	0.0	16172.040000

	Unnamed: 0	datetime
70075	70075	2019-09-30 22:45:00
70076	70076	2019-09-30 23:00:00
70077	70077	2019-09-30 23:15:00
70078	70078	2019-09-30 23:30:00
70079	70079	2019-09-30 23:45:00

	Unnamed: 0	ghi	gti	power
datetime
2017-10-01 00:00:00	0	0.0	0.0	0.0
2017-10-01 00:15:00	1	0.0	0.0	0.0
2017-10-01 00:30:00	2	0.0	0.0	0.0
2017-10-01 00:45:00	3	0.0	0.0	0.0
2017-10-01 01:00:00	4	0.0	0.0	0.0
...	...	...	...	...
2019-09-30 22:45:00	70075	0.0	0.0	0.0
2019-09-30 23:00:00	70076	0.0	0.0	0.0
2019-09-30 23:15:00	70077	0.0	0.0	0.0
2019-09-30 23:30:00	70078	0.0	0.0	0.0
2019-09-30 23:45:00	70079	0.0	0.0	0.0

	Unnamed: 0	plant_id	datetime_utc	datetime_local	cloud_cover	apparent_temperature	temperature	humidity	dew_point	wind_bearing	...	precip_accumulation	precip_intensity	precip_probability	precip_type	visibility	sunrise	sunset	icon	summary	updated_at
0	0	1	2017-09-30 18:30:00	2017-10-01 00:00:00	0.00	26.52	26	1	19.46	308	...	-9999.0	-9999.0	-9999.0	rain	3.57	2017-10-01 00:52:02	2017-10-01 12:45:07	clear-night	Clear	2019-07-16 16:37:16
1	1	1	2017-09-30 19:30:00	2017-10-01 01:00:00	0.00	28.78	27	1	20.06	-9999	...	-9999.0	-9999.0	-9999.0	rain	3.06	2017-10-01 00:52:02	2017-10-01 12:45:07	fog	Foggy	2019-07-16 16:37:16
2	2	1	2017-09-30 20:30:00	2017-10-01 02:00:00	0.00	26.58	26	1	19.97	-9999	...	-9999.0	-9999.0	-9999.0	rain	3.06	2017-10-01 00:52:02	2017-10-01 12:45:07	fog	Foggy	2019-07-16 16:37:16
3	3	1	2017-09-30 21:30:00	2017-10-01 03:00:00	0.00	23.84	23	1	18.66	270	...	-9999.0	-9999.0	-9999.0	rain	3.56	2017-10-01 00:52:02	2017-10-01 12:45:07	clear-night	Clear	2019-07-16 16:37:16
4	4	1	2017-09-30 22:30:00	2017-10-01 04:00:00	0.00	24.13	24	1	17.84	-9999	...	-9999.0	-9999.0	-9999.0	rain	3.06	2017-10-01 00:52:02	2017-10-01 12:45:07	fog	Foggy	2019-07-16 16:37:16
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
13614	13614	1	2019-09-30 13:30:00	2019-09-30 19:00:00	0.16	31.66	29	1	21.19	40	...	NaN	0.0	0.0	NaN	16.09	2019-09-30 00:51:16	2019-09-30 12:46:54	partly-cloudy-night	Partly Cloudy	2019-10-01 23:32:46
13615	13615	1	2019-09-30 14:30:00	2019-09-30 20:00:00	0.16	30.42	28	1	21.35	59	...	NaN	0.0	0.0	NaN	16.09	2019-09-30 00:51:16	2019-09-30 12:46:54	partly-cloudy-night	Partly Cloudy	2019-10-01 23:32:46
13616	13616	1	2019-09-30 15:30:00	2019-09-30 21:00:00	0.18	29.56	27	1	21.61	76	...	NaN	0.0	0.0	NaN	16.09	2019-09-30 00:51:16	2019-09-30 12:46:54	partly-cloudy-night	Partly Cloudy	2019-10-01 23:32:46
13617	13617	1	2019-09-30 16:30:00	2019-09-30 22:00:00	0.30	28.97	27	1	21.99	88	...	NaN	0.0	0.0	NaN	16.09	2019-09-30 00:51:16	2019-09-30 12:46:54	partly-cloudy-night	Partly Cloudy	2019-10-01 23:32:46
13618	13618	1	2019-09-30 17:30:00	2019-09-30 23:00:00	0.35	28.15	26	1	22.09	95	...	NaN	0.0	0.0	NaN	16.09	2019-09-30 00:51:16	2019-09-30 12:46:54	partly-cloudy-night	Partly Cloudy	2019-10-01 23:32:46

	cloud_cover	apparent_temperature	temperature	humidity	dew_point	wind_bearing	wind_speed	wind_chill	wind_gust	heat_index	...	pop	fctcode	ozone	precip_accumulation	precip_intensity	precip_probability	precip_type	visibility	icon	summary
count	13191.000000	13619.000000	13619.000000	13619.000000	13619.000000	12700.000000	12879.000000	0.0	10355.000000	0.0	...	0.0	0.0	10355.000000	0.0	10355.000000	10355.000000	6490.000000	13443.000000	13619.000000	13619.000000
mean	0.237562	25.944639	24.498054	0.537411	11.924626	188.786929	2.638890	NaN	4.245261	NaN	...	NaN	NaN	274.459707	NaN	0.058259	0.018400	0.428831	12.290986	0.182920	0.234222
std	0.342890	10.469808	8.840016	0.498617	9.129459	110.879924	1.290817	NaN	2.177424	NaN	...	NaN	NaN	15.247242	NaN	0.251135	0.055627	0.095356	6.211356	0.053470	0.156872
min	0.000000	3.140000	5.000000	0.000000	-20.560000	0.000000	0.000000	NaN	0.630000	NaN	...	NaN	NaN	225.800000	NaN	0.000000	0.000000	0.025185	0.000000	0.000147	0.000073
25%	0.000000	17.310000	17.000000	0.000000	5.920000	91.000000	1.760000	NaN	2.695000	NaN	...	NaN	NaN	267.800000	NaN	0.000000	0.000000	0.451355	3.670000	0.129819	0.060651
50%	0.010000	25.540000	25.000000	1.000000	10.710000	177.000000	2.520000	NaN	3.740000	NaN	...	NaN	NaN	275.600000	NaN	0.000000	0.000000	0.451355	16.090000	0.214406	0.214259
75%	0.430000	34.580000	31.000000	1.000000	19.670000	298.000000	3.400000	NaN	5.340000	NaN	...	NaN	NaN	283.900000	NaN	0.010000	0.010000	0.451355	16.090000	0.220060	0.408767
max	1.000000	49.750000	47.000000	1.000000	28.350000	359.000000	9.730000	NaN	15.150000	NaN	...	NaN	NaN	351.700000	NaN	6.220000	0.730000	0.451355	16.090000	0.225788	0.408767

	power	cloud_cover	apparent_temperature	temperature	humidity	dew_point	wind_bearing	wind_speed	wind_gust	pressure	uv_index	ozone	precip_intensity	precip_probability	precip_type	visibility	icon	summary
count	17520.000000	17520.000000	17520.000000	17520.000000	17520.000000	17520.000000	17520.000000	17520.000000	17520.000000	17520.000000	17520.000000	17520.000000	17520.000000	17520.000000	17520.000000	17520.000000	17520.000000	17520.000000
mean	28.612915	0.178863	25.245933	24.145320	0.427340	10.444118	211.535902	2.268933	3.176858	885.228147	1.105993	228.155667	0.034433	0.010875	0.164461	9.430807	0.192465	0.273086
std	487.944707	0.314675	9.325530	7.824069	0.494706	8.536539	122.691590	1.297273	2.321461	331.679424	2.430003	110.158910	0.195179	0.043711	0.211153	7.521820	0.050404	0.156213
min	0.000000	0.000000	3.140000	5.000000	0.000000	-20.560000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000147	0.000073
25%	0.000000	0.000000	19.770000	20.000000	0.000000	4.980000	97.000000	1.490000	1.920000	1000.740000	0.000000	254.800000	0.000000	0.000000	0.000000	0.640000	0.129819	0.086570
50%	0.000000	0.000000	22.910000	23.000000	0.000000	7.880000	260.000000	1.990000	3.070000	1010.460000	0.000000	276.500000	0.000000	0.000000	0.025185	16.090000	0.220060	0.408767
75%	5.852500	0.230000	32.152500	30.000000	1.000000	16.290000	335.000000	3.050000	4.120000	1012.980000	0.000000	294.705000	0.000000	0.000000	0.451355	16.090000	0.225788	0.408767
max	15091.685000	1.000000	49.750000	47.000000	1.000000	28.350000	359.000000	9.730000	15.150000	1025.690000	12.000000	351.700000	6.220000	0.730000	0.451355	16.090000	0.225788	0.408767

	cloud_cover	apparent_temperature	temperature	humidity	dew_point	wind_bearing	wind_speed	wind_gust	pressure	uv_index	ozone	precip_intensity	precip_probability	precip_type	visibility	icon	summary
datetime_local
2019-10-01 00:00:00	0.35	26.41	26	1	22.01	101	3.08	6.42	1008.45	0	268.6	0.0	0.0	0.824074	16.09	0.098765	0.112654
2019-10-01 01:00:00	0.43	25.95	25	1	22.06	105	2.94	6.28	1008.24	0	269.1	0.0	0.0	0.824074	16.09	0.098765	0.112654
2019-10-01 02:00:00	0.48	25.46	25	1	22.06	106	2.82	6.08	1007.90	0	269.7	0.0	0.0	0.824074	16.09	0.098765	0.112654
2019-10-01 03:00:00	0.49	24.80	24	1	21.87	106	2.63	5.74	1007.61	0	270.4	0.0	0.0	0.824074	16.09	0.098765	0.112654
2019-10-01 04:00:00	0.49	23.99	23	1	21.53	100	2.39	5.30	1007.51	0	271.3	0.0	0.0	0.824074	16.09	0.098765	0.112654

	power	cloud_cover	apparent_temperature	temperature	humidity	dew_point	wind_bearing	wind_speed	wind_gust	pressure	uv_index	ozone	precip_intensity	precip_probability	precip_type	visibility	icon	summary	power_pred	power_pred1
datetime
2019-08-01 00:00:00	0.0	0.99	36.36	30	1	25.01	121.0	3.45	6.34	999.06	0.0	280.3	0.00	0.00	0.000000	16.09	0.095822	0.052427	0.338723	0.186789
2019-08-01 01:00:00	0.0	0.99	35.66	30	1	24.94	126.0	3.52	6.44	998.69	0.0	280.3	0.00	0.00	0.000000	16.09	0.095822	0.052427	0.325856	0.178427
2019-08-01 02:00:00	0.0	0.99	34.88	29	1	25.01	128.0	3.55	6.49	998.46	0.0	280.2	0.00	0.00	0.000000	16.09	0.095822	0.052427	0.385216	0.193334
2019-08-01 03:00:00	0.0	0.98	33.43	28	1	25.14	124.0	3.46	6.53	998.59	0.0	280.3	0.07	0.09	0.451355	16.09	0.095822	0.052427	0.402874	0.190655
2019-08-01 04:00:00	0.0	0.95	31.50	27	1	25.20	114.0	3.25	6.55	998.78	0.0	280.4	0.36	0.17	0.451355	16.09	0.095822	0.052427	0.528821	0.294492